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---
title: DALL·E mini
emoji: 🥑
colorFrom: yellow
colorTo: green
sdk: streamlit
app_file: app/app.py
pinned: false
---
# DALL·E Mini
_Generate images from a text prompt_
<img src="img/logo.png" width="200">
Our logo was generated with DALL·E mini using the prompt "logo of an armchair in the shape of an avocado".
You can create your own pictures with [the demo](https://huggingface.co/spaces/flax-community/dalle-mini).
## How does it work?
Refer to [our report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA).
## Development
### Dependencies Installation
The root folder and associated [`requirements.txt`](./requirements.txt) is only for the app.
For development, use [`dev/requirements.txt`](dev/requirements.txt) or [`dev/environment.yaml`](dev/environment.yaml).
### Training of VQGAN
The VQGAN was trained using [taming-transformers](https://github.com/CompVis/taming-transformers).
We recommend using the latest version available.
### Conversion of VQGAN to JAX
Use [patil-suraj/vqgan-jax](https://github.com/patil-suraj/vqgan-jax).
### Training of Seq2Seq
Refer to [`dev/seq2seq`](dev/seq2seq) folder.
You can also adjust the [sweep configuration file](https://docs.wandb.ai/guides/sweeps) if you need to perform a hyperparameter search.
### Inference Pipeline
To generate sample predictions and understand the inference pipeline step by step, refer to [`dev/inference/inference_pipeline.ipynb`](dev/inference/inference_pipeline.ipynb).
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/dev/inference/inference_pipeline.ipynb)
## FAQ
### Where to find the latest models?
Trained models are on 🤗 Model Hub:
- [VQGAN-f16-16384](https://huggingface.co/flax-community/vqgan_f16_16384) for encoding/decoding images
- [DALL·E mini](https://huggingface.co/flax-community/dalle-mini) for generating images from a text prompt
### Where does the logo come from?
The "armchair in the shape of an avocado" was used by OpenAI when releasing DALL·E to illustrate the model's capabilities. Having successful predictions on this prompt represents a big milestone to us.
## Authors
- [Boris Dayma](https://github.com/borisdayma)
- [Suraj Patil](https://github.com/patil-suraj)
- [Pedro Cuenca](https://github.com/pcuenca)
- [Khalid Saifullah](https://github.com/khalidsaifullaah)
- [Tanishq Abraham](https://github.com/tmabraham)
- [Phúc Lê Khắc](https://github.com/lkhphuc)
- [Luke Melas](https://github.com/lukemelas)
- [Ritobrata Ghosh](https://github.com/ghosh-r)
## Acknowledgements
- 🤗 Hugging Face for organizing [the FLAX/JAX community week](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects)
- Google Cloud team for providing access to TPU's